A strong (weak) East Asian summer monsoon (EASM) is usually concurrent with the tripole pattern of North Atlantic SST anomalies on the interannual timescale during summer, which has positive (negative) SST anomalies in the northwestern North Atlantic and negative (positive) SST anomalies in the subpolar and tropical ocean. The mechanisms responsible for this linkage are diagnosed in the present study. It is shown that a barotropic wave-train pattern occurring over the Atlantic-Eurasia region likely acts as a link between the EASM and the SST tripole during summer. This wave-train pattern is concurrent with geopotential height anomalies over the Ural Mountains, which has a substantial effect on the EASM. Diagnosis based on observations and linear dynamical model results reveals that the mechanism for maintaining the wave-train pattern involves both the anomalous diabatic heating and synoptic eddy-vorticity forcing. Since the North Atlantic SST tripole is closely coupled with the North Atlantic Oscillation (NAO), the relationships between these two factors and the EASM are also examined. It is found that the connection of the EASM with the summer SST tripole is sensitive to the meridional location of the tripole, which is characterized by large seasonal variations due to the north-south movement of the activity centers of the NAO. The SST tripole that has a strong relationship with the EASM appears to be closely coupled with the NAO in the previous spring rather than in the simultaneous summer.
Climate variability modes, usually known as primary climate phenomena, are well recognized as the most important predictability sources in subseasonal–interannual climate prediction. This paper begins by reviewing the research and development carried out, and the recent progress made, at the Beijing Climate Center(BCC) in predicting some primary climate variability modes. These include the El Ni?o–Southern Oscillation(ENSO), Madden–Julian Oscillation(MJO), and Arctic Oscillation(AO), on global scales, as well as the sea surface temperature(SST) modes in the Indian Ocean and North Atlantic, western Pacific subtropical high(WPSH), and the East Asian winter and summer monsoons(EAWM and EASM, respectively), on regional scales. Based on its latest climate and statistical models, the BCC has established a climate phenomenon prediction system(CPPS) and completed a hindcast experiment for the period 1991–2014. The performance of the CPPS in predicting such climate variability modes is systematically evaluated. The results show that skillful predictions have been made for ENSO, MJO, the Indian Ocean basin mode, the WPSH, and partly for the EASM, whereas less skillful predictions were made for the Indian Ocean Dipole(IOD) and North Atlantic SST Tripole, and no clear skill at all for the AO, subtropical IOD, and EAWM. Improvements in the prediction of these climate variability modes with low skill need to be achieved by improving the BCC's climate models, developing physically based statistical models as well as correction methods for model predictions.Some of the monitoring/prediction products of the BCC-CPPS are also introduced in this paper.
By using the nonlinear local Lyapunov exponent and nonlinear error growth dynamics, the predictability limit of monthly precipitation is quantitatively estimated based on daily observations collected from approximately 500 stations in China for the period 1960–2012. As daily precipitation data are not continuous in space and time, a transformation is first applied and a monthly standardized precipitation index(SPI) with Gaussian distribution is constructed. The monthly SPI predictability limit(MSPL) is quantitatively calculated for SPI dry, wet, and neutral phases. The results show that the annual mean MSPL varies regionally for both wet and dry phases: the MSPL in the wet(dry) phase is relatively higher(lower) in southern China than in other regions. Further, the pattern of the MSPL for the wet phase is almost opposite to that for the dry phase in both autumn and winter. The MSPL in the dry phase is higher in winter and lower in spring and autumn in southern China, while the MSPL values in the wet phase are higher in summer and winter than those in spring and autumn in southern China. The spatial distribution of the MSPL resembles that of the prediction skill of monthly precipitation from a dynamic extended-range forecast system.
This study evaluates the performance of CAMS-CSM(the climate system model of the Chinese Academy of Meteorological Sciences) in simulating the features, dynamics, and teleconnections to East Asian climate of the El Ni?o–Southern Oscillation(ENSO). In general, fundamental features of ENSO, such as its dominant patterns and phase-locking features, are reproduced well. The two types of El Ni?o are also represented, in terms of their spatial distributions and mutual independency. However, the skewed feature is missed in the model and the simulation of ENSO is extremely strong, which is found—based on Bjerknes index assessment—to be caused by underestimation of the shortwave damping effect. Besides, the modeled ENSO exhibits a regular oscillation with a period shorter than observed. By utilizing the Wyrtki index, it is suggested that this periodicity bias results from an overly quick phase transition induced by feedback from the thermocline and zonal advection. In addition to internal dynamics of ENSO,its external precursors—such as the North Pacific Oscillation with its accompanying seasonal footprinting mechanism, and the Indian Ocean Dipole with its 1-yr lead correlation with ENSO—are reproduced well by the model. Furthermore, with respect to the impacts of ENSO on the East Asian summer monsoon, although the anomalous Philippine anticyclone is reproduced in the post-El Ni?o summer, it exhibits an eastward shift compared with observation;and as a consequence, the observed flooding of the Yangtze River basin is poorly represented, with unrealistic air–sea interaction over the South China Sea being the likely physical origin of this bias. The response of wintertime lowertropospheric circulation to ENSO is simulated well, in spite of an underestimation of temperature anomalies in central China. This study highlights the dynamic processes that are key for the simulation of ENSO, which could shed some light on improving this model in the future.
The temporal variability and spatial pattern of the Arctic Oscillation(AO)simulated in the historical experiment of26 coupled climate models participating in the Coupled Model Intercomparison Project Phase 5(CMIP5)are evaluated.Spectral analysis of the monthly AO index indicates that 23 out of the 26 CMIP5 models exhibit no statistically significant spectral peak in the historical experiment,as seen in the observations.These models are able to reproduce the AO pattern in the sea level pressure anomaly field during boreal winter,but the intensity of the AO pattern tends to be overestimated in all the models.The zonal-mean zonal wind anomalies associated with the AO is dominated by a meridional dipole in the mid-high latitudes of the Northern Hemisphere during boreal winter,which is well reproduced by only a few models.Most models show significant biases in both strength and location of the dipole compared to the observation.In considering the temporal variability as well as spatial structures in both horizontal and vertical directions,the MPI-ESM-P model reproduces an AO pattern that resembles the observation the best.
